
"""simple docstring"""



import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed




class 		a_    (			_UpperCAmelCase						):







    def __init__(      self      :							List[Any]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							str=10_24  ,       __UpperCamelCase      :							Optional[int]=10_24  ,       __UpperCamelCase      :							Dict=3.6  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = tokenizer
        _UpperCAmelCase        = tokenizer.bos_token_id
        _UpperCAmelCase        = dataset
        _UpperCAmelCase        = seq_length
        _UpperCAmelCase        = seq_length * chars_per_token * num_of_sequences







    def __iter__(      self      :							int  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = iter(self.dataset  )
        _UpperCAmelCase        = True
        while more_examples:
            _UpperCAmelCase     ,_UpperCAmelCase        = [], 0
            while True:
                if buffer_len >= self.input_characters:
                    break
                try:
                    buffer.append(next(__UpperCamelCase  )["""content"""]  )
                    buffer_len += len(buffer[-1]  )
                except StopIteration:
                    _UpperCAmelCase        = False
                    break
            _UpperCAmelCase        = tokenizer(__UpperCamelCase  ,       truncation=__UpperCamelCase  )["""input_ids"""]
            _UpperCAmelCase        = []
            for tokenized_input in tokenized_inputs:
                all_token_ids.extend(tokenized_input + [self.concat_token_id]  )
            for i in range(0  ,       len(__UpperCamelCase  )  ,       self.seq_length  ):
                _UpperCAmelCase        = all_token_ids[i : i + self.seq_length]
                if len(__UpperCamelCase  ) == self.seq_length:
                    yield torch.tensor(__UpperCamelCase  )







def 			_UpperCamelCase						(    _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = {"""streaming""": True}
    _UpperCAmelCase        = load_dataset(args.dataset_name       ,   split="""train"""       ,   **_A   )
    _UpperCAmelCase        = ConstantLengthDataset(_A       ,   _A       ,   seq_length=args.seq_length   )
    _UpperCAmelCase        = DataLoader(_A       ,   batch_size=args.batch_size   )
    return eval_dataloader


def 			_UpperCamelCase						(    _A   )     ->	List[Any]:





    """simple docstring"""

    model.eval()
    _UpperCAmelCase        = []
    for step, batch in enumerate(_A   ):
        with torch.no_grad():
            _UpperCAmelCase        = model(_A       ,   labels=_A   )
        _UpperCAmelCase        = outputs.loss.repeat(args.batch_size   )
        losses.append(accelerator.gather(_A   )   )

        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    _UpperCAmelCase        = torch.mean(torch.cat(_A   )   )
    try:
        _UpperCAmelCase        = torch.exp(_A   )
    except OverflowError:
        _UpperCAmelCase        = float("""inf"""   )
    return loss.item(), perplexity.item()


# Setup Accelerator
a       :						List[str]       		=		Accelerator()

# Parse configuration
a       :						Union[str, Any]       		=		HfArgumentParser(EvaluationArguments)
a       :						List[str]       		=		parser.parse_args()
set_seed(args.seed)

# Logging
a       :						Union[str, Any]       		=		logging.getLogger(__name__)
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)

# Load model and tokenizer
a       :						int       		=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a       :						List[str]       		=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a       :						Optional[int]       		=		create_dataloader(args)

# Prepare everything with our `accelerator`.
a		,			a       :						Union[str, Any]       		=		accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
a		,			a       :						str       		=		evaluate(args)







logger.info(F"loss/eval: {eval_loss}, perplexity: {perplexity}")

"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.generation import DisjunctiveConstraint




@require_torch
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Union[str, Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2, 4], [1, 2, 3, 4]]
        _UpperCAmelCase        = DisjunctiveConstraint(__UpperCamelCase  )
        self.assertTrue(isinstance(dc.token_ids  ,       __UpperCamelCase  )  )

        with self.assertRaises(__UpperCamelCase  ):
            DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]  )  )

        with self.assertRaises(__UpperCamelCase  ):
            DisjunctiveConstraint([torch.LongTensor([1, 2, 4]  ), torch.LongTensor([1, 2, 3, 4, 5]  )]  )







    def   _snake_case       (      self      :							Optional[int]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2], [1, 2, 3, 4]]

        with self.assertRaises(__UpperCamelCase  ):
            DisjunctiveConstraint(__UpperCamelCase  )  # fails here







    def   _snake_case       (      self      :							Optional[int]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2, 3], [1, 2, 4]]

        _UpperCAmelCase        = DisjunctiveConstraint(__UpperCamelCase  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(1  )
        _UpperCAmelCase        = stepped is True and completed is False and reset is False
        self.assertTrue(__UpperCamelCase  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(2  )
        _UpperCAmelCase        = stepped is True and completed is False and reset is False
        self.assertTrue(__UpperCamelCase  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1, 2]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(3  )
        _UpperCAmelCase        = stepped is True and completed is True and reset is False
        self.assertTrue(__UpperCamelCase  )
        self.assertTrue(dc.completed  )  # Completed!
        self.assertTrue(dc.current_seq == [1, 2, 3]  )







    def   _snake_case       (      self      :							List[Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

        _UpperCAmelCase        = DisjunctiveConstraint(__UpperCamelCase  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(1  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(2  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1, 2]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(4  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1, 2, 4]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(5  )
        self.assertTrue(dc.completed  )  # Completed!
        self.assertTrue(dc.current_seq == [1, 2, 4, 5]  )

        dc.reset()

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(1  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.remaining() == 3  )
        self.assertTrue(dc.current_seq == [1]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(2  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.remaining() == 2  )
        self.assertTrue(dc.current_seq == [1, 2]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(5  )
        self.assertTrue(dc.completed  )  # Completed!
        self.assertTrue(dc.remaining() == 0  )
        self.assertTrue(dc.current_seq == [1, 2, 5]  )